scholarly journals Call for papers: Special issue on unlocking genetic diseases by integrating machine learning techniques and medical data

2021 ◽  
Vol 4 (3) ◽  
pp. 221-221
Keyword(s):  
Machine Learning ◽  
Genetic Diseases ◽  
Medical Data ◽  
Machine Learning Techniques ◽  
Special Issue ◽  
Learning Techniques

scholarly journals Collaboration of Blockchain and Machine Learning in Healthcare Industry

2019 ◽  
Vol 9 (1) ◽  
pp. 2642-2645
Keyword(s):  
Machine Learning ◽  
Medical Data ◽  
Machine Learning Techniques ◽  
Insurance Companies ◽  
Healthcare Sector ◽  
Healthcare Industry ◽  
Sensitive Data ◽  
Medical Practitioners ◽  
Blockchain Technology ◽  
Learning Techniques

The purpose of this paper is to explore the applications of blockchain in the healthcare industry. Healthcare sector can become an application domain of blockchain as it can be used to securely store health records and maintain an immutable version of truth. Blockchain technology is originally built on Hyperledger, which is a decentralized platform to enable secure, unambiguous and swift transactions and usage of medical records for various purposes. The paper proposes to use blockchain technology to provide a common and secured platform through which medical data can be accessed by doctors, medical practitioners, pharma and insurance companies. In order to provide secured access to such sensitive data, blockchain ensures that any organization or person can only access data with consent of the patient. The Hyperledger Fabric architecture guarantees that the data is safe and private by permitting the patients to grant multi-level access to their data. Apart from blockchain technology, machine learning can be used in the healthcare sector to understand and analyze patterns and gain insights from data. As blockchain can be used to provide secured and authenticated data, machine learning can be used to analyze the provided data and establish new boundaries by applying various machine learning techniques on such real-time medical data.

scholarly journals CRISPRpred(SEQ): a sequence based tool for sgRNA on target activity prediction [(almost) beating Deep Learning pipelines by traditional machine learning]

2019 ◽  
Author(s):  
Ali Haisam Muhammad Rafid ◽  
Md. Toufikuzzaman ◽  
Mohammad Saifur Rahman ◽  
M. Sohel Rahman
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Genome Editing ◽  
Cell Lines ◽  
Genetic Diseases ◽  
Machine Learning Techniques ◽  
Activity Prediction ◽  
Current State ◽  
Learning Techniques ◽  
Target Activity

AbstractAn accurate and fast genome editing tool can be used to treat genetic diseases, modify crops genetically etc. However, a tool that has low accuracy can be risky to use, as incorrect genome editing may have severe consequences. Although many tools have been developed in the past, there are still room for further improvement. In this paper, we present CRISPRpred(SEQ), a sequence based tool for sgRNA on target activity prediction that leverages only traditional machine learning techniques. We compare the results of CRISPRpred(SEQ) with that of DeepCRISPR, the current state-of-the-art, which uses a deep learning pipeline. In spite of using only traditional machine learning methods, we are able to beat DeepCRISPR for the three out of four cell lines in the benchmark dataset convincingly (2.174%, 6.905% and 8.119% improvement for the three cell lines), which is quite outstanding.

Data Analytics to Predict, Detect, and Monitor Chronic Autoimmune Diseases Using Machine Learning Algorithms

2021 ◽  
pp. 150-182
Author(s):  
Jayashree M. Kudari
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Medical Data ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Data Set ◽  
Huge Data ◽  
Learning Techniques ◽  
Classification And Regression ◽  
Robust Prediction

Developments in machine learning techniques for classification and regression exposed the access of detecting sophisticated patterns from various domain-penetrating data. In biomedical applications, enormous amounts of medical data are produced and collected to predict disease type and stage of the disease. Detection and prediction of diseases, such as diabetes, lung cancer, brain cancer, heart disease, and liver diseases, requires huge tests and that increases the size of patient medical data. Robust prediction of a patient's disease from the huge data set is an important agenda in in this chapter. The challenge of applying a machine learning method is to select the best algorithm within the disease prediction framework. This chapter opts for robust machine learning algorithms for various diseases by using case studies. This usually analyzes each dimension of disease, independently checking the identified value between the limits to monitor the condition of the disease.

Special Issue on Machine Learning for Robotics and Swarm Systems

2019 ◽  
Vol 31 (4) ◽  
pp. 519-519
Author(s):  
Masahito Yamamoto ◽  
Takashi Kawakami ◽  
Keitaro Naruse
Keyword(s):  
Machine Learning ◽  
Autonomous Agents ◽  
Autonomous Robots ◽  
Machine Learning Techniques ◽  
Multi Agent Systems ◽  
Special Issue ◽  
Learning Techniques ◽  
Machine Learning Applications ◽  
Multi Agent ◽  
The Relationship

In recent years, machine-learning applications have been rapidly expanding in the fields of robotics and swarm systems, including multi-agent systems. Swarm systems were developed in the field of robotics as a kind of distributed autonomous robotic systems, imbibing the concepts of the emergent methodology for extremely redundant systems. They typically consist of homogeneous autonomous robots, which resemble living animals that build swarms. Machine-learning techniques such as deep learning have played a remarkable role in controlling robotic behaviors in the real world or multi-agents in the simulation environment. In this special issue, we highlight five interesting papers that cover topics ranging from the analysis of the relationship between the congestion among autonomous robots and the task performances, to the decision making process among multiple autonomous agents. We thank the authors and reviewers of the papers and hope that this special issue encourages readers to explore recent topics and future studies in machine-learning applications for robotics and swarm systems.

scholarly journals Special Issue on Machine Learning Techniques Applied to Geoscience Information System and Remote Sensing

2019 ◽  
Vol 9 (12) ◽  
pp. 2446 ◽  
Author(s):  
Hyung-Sup Jung ◽  
Saro Lee
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Information System ◽  
Information Systems ◽  
Machine Learning Techniques ◽  
Special Issue ◽  
Geospatial Information ◽  
Gis And Remote Sensing ◽  
Learning Techniques

As computer and space technologies have been developed, geoscience information systems (GIS) and remote sensing (RS) technologies, which deal with the geospatial information, have been maturing rapidly [...]

Sign in / Sign up

Export Citation Format

Share Document